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Dhakal, Rabin Accepted Thesis FA24.pdf (52.1 MB)

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Towards a Low-Cost Distributed AWOS: Machine Learning for Optical Ceilometry, Cloud Detection, and Classification

Dhakal, Rabin
http://orcid.org/0009-0001-2245-8077
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1732637837288324

Abstract Details

2024, Master of Science (MS), Ohio University, Computer Science (Engineering and Technology).
Larger, commercial, towered airports are highly equipped to provide pilots with real-time weather related data before flying the aircraft. In the case of small airports, there is a weather data gap for the aircraft flying at a lower altitude. Accurate cloud information (cloud type and its height from the ground) is crucial data for pilots flying at low altitudes because it affects both visibility and safety. A ceilometer is a device that estimates cloud height from the ground, but it is often costly and lacks portability. This thesis proposes an innovative, cost-effective approach using computer vision and deep learning to address these limitations. One of the primary challenges for these methods is the need for extensive datasets for training and evaluation, as real-world data collection of cloud height and type is time-consuming and resource-intensive. To overcome this, we generated synthetic cloud data using a stereo camera setup with ground truth height information in a virtual environment. In this thesis, cloud information involves cloud-base height estimation and classification of the type of cloud. We proposed methods that can provide better efficiency in predicting the cloud-base height than state-of-the-art methods when applied to the real-world dataset in the future. We have incorporated synthetic data to evaluate the performance of our method. These synthetic data, created by simulating VDB clouds, enable the testing and validation of cloud detection models and calibrating height predictions. We rendered the 3D scene and created ground truth bounding box and cloud-type datasets, such as Altocumulus, Altostratus, Cirrocumulus, Cumulonimbus, Cumulus, Cirrostratus, Cirrus, Stratocumulus, and Stratus. We trained the YOLO-v8 model with the cloud detection dataset and employed unseen synthetic data to assess its robustness and accuracy. Once vetted, we generated disparity images from the stereo pairs. We trained several CNN-based regression models using this disparity dataset. Starting with a simple CNN, we gradually progressed to more complex feature-extracting models, exploring how well these models could predict cloud height from the synthetic disparity images. ResNet-18 backbone achieved an RMSE of 193.91 meters. These experiments conducted in the virtual environment suggest that our camera-based method can provide viable, low-cost alternatives to existing Automated Weather Observing Systems (AWOS) in estimating cloud information.
Chad Mourning (Advisor)
Zhewei Wang (Committee Member)
Jundong Liu (Committee Member)
Bhaven Naik (Committee Member)
94 p.
Computer Science
UAV; Stereo Matching; CNN; Volumetric Rendering

Recommended Citations

Citations

  • Dhakal, R. (2024). Towards a Low-Cost Distributed AWOS: Machine Learning for Optical Ceilometry, Cloud Detection, and Classification [Master's thesis, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1732637837288324

    APA Style (7th edition)

  • Dhakal, Rabin. Towards a Low-Cost Distributed AWOS: Machine Learning for Optical Ceilometry, Cloud Detection, and Classification. 2024. Ohio University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1732637837288324.

    MLA Style (8th edition)

  • Dhakal, Rabin. "Towards a Low-Cost Distributed AWOS: Machine Learning for Optical Ceilometry, Cloud Detection, and Classification." Master's thesis, Ohio University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1732637837288324

    Chicago Manual of Style (17th edition)

ohiou1732637837288324
28
© 2024, all rights reserved.
This open access ETD is published by Ohio University and OhioLINK.